This paper presents a change detection system using Planet's mosaic dataset. This dataset has higher resolution but fewer bands than data captured from Landsat or Sentinel satellites. Here, an object-based random forest regressor is used to detect vegetation change. The mosaic was separated into individual images, which were then ranked in a list. Results indicate adequate performance with a tradeoff in precision and recall.

Clustering can help to make large datasets more manageable by grouping together similar objects. However, most clustering approaches are unable to scale to very large datasets (e.g. more than 10 million objects). The K-Tree is a data structure and clustering algorithm that has proven to be scalable with large streaming datasets. Here, we apply the K-Tree to spatial data (satellite images) and extend from a single threaded to a multicore environment. We show that the K-Tree is able to cluster larger dataset more efficiently than baseline approaches.

Location-aware social media is increasing being used to inform decisions in a spatiotemporal context. However, collecting, fusing, processing and merging information from different social media platforms is a challenge because of diversity of information between different platforms. Here, we present the WIMBY, which is able to access multiple social media platforms to help users answer the question "What's in my Backyard?". In doing so, the WIMBY helps to address the challenge of dealing with diverse social media information. It is believed that the WIMBY can be extended to include more information sources (including other social media platforms) to help inform decision makers in a wider array of applications.

Different information retrieval (IR) systems often return very diverse results lists for the same query. This is problematic for users since no one IR system works best for every scenario, and it is difficult for the user to know which system will work best a priori. The challenge of metasearch is to merge results lists from several IR systems, with the goal of outperforming each of the constituent systems. This paper presents ComRank, a metasearch system that discriminates in favour of results that (1) originate by consensus amongst several systems; (2) are highly ranked in their original systems and (3) originate from the better performing systems. Importantly, ComRank determines the `better' performing systems without the need for human judgements. Rather, it uses an automatic assessment process that ranks systems by their pseudo-relevance, as derived from highly ranked results in ComRank's list. We apply our methods to the INEX Collection, which is an unexplored domain for these methods, and show that they are comparable to or better than baseline alternatives

XML information retrieval (XML-IR) systems aim to provide users with highly exhaustive and highly specific results. To interact with XML-IR systems, users must express both their content and structural requirement, in the form of a structured query. Traditionally, these structured queries have been formatted using formal languages such as XPath or NEXI. Unfortunately, formal query languages are very complex and too difficult to be used by experienced, let alone casual users. Therefore, recent research has investigated the idea of specifying users' content and structural needs via natural language queries (NLQs). In previous research we developed NLPX, a natural language interface to an XML-IR system. Here we present additions we have made to NLPX. The additions involve the application of transformation-based error-driven learning (TBL) to structured NLQs, to derive special connotations and group words into an atomic unit of information. TBL has successfully been applied to other areas of natural language processing; however, this paper presents the first time it has been applied to structured NLQs. Here, we investigate the applicability of TBL to NLQs and compare the TBL-based system, with our previous system and a system with a formal language interference. Our results show that TBL is effective for structured NLQs, and that structured NLQs a viable interface tor XML-IR systems